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Learner Reviews & Feedback for Hierarchical Clustering: Customer Segmentation by Rhyme

4.4
stars
14 ratings
4 reviews

About the Course

In this 1-hour long project-based course, you will learn how to use Python to implement a Hierarchical Clustering algorithm, which is also known as hierarchical cluster analysis. This type of algorithm groups objects of similar behavior into groups or clusters. The output of this model is a set of visualized clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other in features. In this project, you will learn the fundamental theory and practical illustrations behind Hierarchical Clustering and learn to fit, examine, and utilize unsupervised Clustering models to examine relationships between unlabeled input features and output variables, using Python. We will walk you step-by-step into Machine Learning unsupervised problems. With every task in this project, you will expand your knowledge, develop new skills and broaden your experience in Machine Learning. Particularly, you will build a Hierarchical Clustering algorithm to apply market segmentation on a group of customers based on several features. By the end of this project, you will be able to build your own Hierarchical Clustering model and make amazing clusters of customers. In order to be successful in this project, you should just know the basics of Python and clustering algorithms....
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1 - 4 of 4 Reviews for Hierarchical Clustering: Customer Segmentation

By Learner

May 19, 2020

This is a beginners level course. The instructor uses scipy and sklearn libraries without explaining them.

By Badr k

May 14, 2020

Perfect Project. Thank You

By Rashmi M

Apr 21, 2020

very good

By Pranay U

Apr 28, 2020

The project scope could have been expanded like adding other hierarchical clustering methods and then maybe differentiating it and then justifying which would be a better choice. However, this project basically highlights the application of hierarchical clustering using a dendrogram.